在构建loss时pytorch常用的包中有最常见的MSE、cross entropy(logsoftmax+NLLLoss)、KL散度Loss、BCE、HingeLoss等等,详见:https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/#loss-functions 这里主要讲解一种考虑类间距离的Center Loss: 一、简介: center loss来自ECCV2016的一篇论文:A Dis...
3.2 Contrastive Loss pytorch # Custom Contrastive Loss class ContrastiveLoss(torch.nn.Module): """ Contrastive loss function. Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf """ def __init__(self, margin=2.0): super(ContrastiveLoss, self).__init__() self...
with this loss, we will optimize the loss function until for positive pair, we want s_p is less than m_pos? for negative pair we want n_p is less than n_pos ? I saw some definition of contrastive loss is: It is a little bit different with your contrastive, right? Can you explain...
损失函数(Loss Function) 定义损失函数的一种常见方法是测量模型预测与固定目标之间的差异(如模型的softmax输出与one-hot标签的交叉熵函数),但由于无监督学习的数据无固定标签,故作者采用对比损失。 对比损失(Contrastive Loss)衡量一个表示空间中样本对的相似性。在对比损失公式中,目标可以在训练过程中动态变化,而不是...
Contrastive loss function - implementation in PyTorch, vectorized version The performance of naive implementation is really poor (mostly due to the manual loop), see the results:Once I understood the internals of the loss, it's easy to vectorize it and remove the manual loop:The difference shoul...
在苏剑林开源的pytorch版代码中,eval.py文件中损失函数simcse_loss的写法很有意思,在其博客(中文任务还是SOTA吗?我们给SimCSE补充了一些实验)中,损失函数被描述为: -\sum_{i=1}^{N}{\sum_{\alpha=0,1}^{}{log\frac{e^{cos(h_{i}^{\alpha},h_{i}^{1-\alpha})/\tau}}{\sum_{j=1,j\ne i...
All experiments are conducted with Pytorch (version 1.7.1) on a computer with Windows 10 Operating System, Intel Core i5-10400F Processor, 16 GB Memory and GeForce RTX3070 GPU. Datasets and metrics The FLIR dataset is chosen as the training set for both stages. In the test phase, we ...
This repository is the official PyTorch implementation of SAINT. Find the paper on arxiv SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training Requirements We recommend using anaconda or miniconda for python. Our code has been tested with python=3.8 on linu...
models, and its implementation in PyTorch provides a flexible and efficient way to incorporate this loss function into your machine learning pipeline. Next time you work on a project that requires feature embedding learning, consider using sphere contrastive loss to boost the performance of your ...
Note that we implement color distortions using the torchvision2 package in PyTorch [31]. 3.2. Contrastive Visual Embedding Using contrastive learning to learn visual embeddings was first explored by Hadsell et al. [32]. Given an image set {I=i1,…,ip},xi∈Rd, the goal of the task is ...